import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
import numpy as np
import glob
%matplotlib inline
#датафреймы с размеченными данными в среднем для песни
#df с динамическим измерением(за каждую секунду) arousal и valence
df_dynamic_arousal = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/arousal.csv', index_col='song_id')
df_dynamic_valence = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/dynamic (per second annotations)/valence.csv', index_col='song_id')
#df со статическими измерениями для всего фрагмента аудио
df_static_averaged_1_2000 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_1_2000.csv', index_col='song_id')
df_static_averaged_2000_2058 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations averaged per song/song_level/static_annotations_averaged_songs_2000_2058.csv',index_col='song_id')
#датафреймы с размеченными данными для каждого оценщика для песни 2.mp3
#df с динамическим измерением(за каждую секунду) arousal и valence
df_dynamic_arousal_2 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/arousal/2.csv')
df_dynamic_valence_2 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/2.csv')
#df со статическими измерениями для всего фрагмента аудио
df_static_annotations_songs_1_2000 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/song_level/static_annotations_songs_1_2000.csv')
#Посмотрим на данные для песни 2.mp3
df_dynamic_arousal_2
| sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | sample_19500ms | ... | sample_40000ms | sample_40500ms | sample_41000ms | sample_41500ms | sample_42000ms | sample_42500ms | sample_43000ms | sample_43500ms | sample_44000ms | sample_44500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | 0.069565 | ... | 0.013043 | 0.013043 | 0.013043 | 0.013043 | 0.013043 | 0.013043 | 0.013043 | 0.013043 | 0.013043 | 0.013043 |
| 1 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | ... | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 | -0.334780 |
| 2 | -0.036811 | -0.017793 | -0.023913 | -0.010870 | -0.029466 | -0.046365 | -0.038966 | -0.103960 | -0.046054 | -0.032225 | ... | 0.090484 | 0.045753 | 0.028261 | 0.058696 | 0.033641 | 0.026854 | 0.034225 | 0.037743 | 0.038966 | 0.027969 |
| 3 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | 0.418840 | ... | -0.642570 | -0.642570 | -0.642570 | -0.642570 | -0.642570 | -0.642570 | -0.642570 | -0.642570 | -0.642570 | -0.642570 |
| 4 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | ... | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 | -0.817390 |
| 5 | -0.422210 | -0.439600 | -0.447440 | -0.447440 | -0.456130 | -0.456130 | -0.456130 | -0.456130 | -0.456130 | -0.456130 | ... | -0.438750 | -0.426940 | -0.436320 | -0.459200 | -0.468490 | -0.472800 | -0.482190 | -0.489450 | -0.511110 | -0.521290 |
| 6 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | -0.350000 | ... | -0.358700 | -0.358700 | -0.358700 | -0.358700 | -0.358700 | -0.358700 | -0.358700 | -0.358700 | -0.358700 | -0.358700 |
| 7 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | ... | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 | 0.508700 |
| 8 | 0.047826 | 0.056522 | 0.056522 | 0.056522 | 0.056522 | 0.006003 | -0.037964 | -0.051545 | -0.052174 | -0.052174 | ... | -0.258610 | -0.252170 | -0.252170 | -0.252170 | -0.252170 | -0.245880 | -0.241520 | -0.230860 | -0.224140 | -0.215460 |
| 9 | -0.177600 | -0.243480 | -0.244230 | -0.279280 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | ... | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 | -0.330430 |
10 rows × 60 columns
df_dynamic_valence_2
| sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | sample_19500ms | ... | sample_40000ms | sample_40500ms | sample_41000ms | sample_41500ms | sample_42000ms | sample_42500ms | sample_43000ms | sample_43500ms | sample_44000ms | sample_44500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | -0.039130 | ... | -0.082609 | -0.082609 | -0.082609 | -0.091304 | -0.091304 | -0.091304 | -0.091304 | -0.091304 | -0.091304 | -0.091304 |
| 1 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | ... | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 | -0.521740 |
| 2 | -0.384860 | -0.399610 | -0.402170 | -0.402170 | -0.402170 | -0.389840 | -0.405020 | -0.390790 | -0.375680 | -0.390280 | ... | -0.493480 | -0.489130 | -0.469510 | -0.478000 | -0.484780 | -0.476090 | -0.467590 | -0.471740 | -0.485570 | -0.489130 |
| 3 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | 0.088233 | ... | -0.372870 | -0.372870 | -0.372870 | -0.372870 | -0.372870 | -0.372870 | -0.372870 | -0.372870 | -0.372870 | -0.372870 |
| 4 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | 0.834780 | ... | -0.639130 | -0.639130 | -0.639130 | -0.639130 | -0.639130 | -0.639130 | -0.639130 | -0.639130 | -0.639130 | -0.639130 |
| 5 | -0.191140 | -0.189590 | -0.181190 | -0.221960 | -0.256300 | -0.261480 | -0.273680 | -0.273250 | -0.283230 | -0.291050 | ... | -0.386620 | -0.390960 | -0.390960 | -0.395310 | -0.395310 | -0.395380 | -0.404000 | -0.404000 | -0.404000 | -0.391030 |
| 6 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | ... | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.319560 | -0.321260 | -0.328260 | -0.328260 |
| 7 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | ... | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 | 0.143480 |
| 8 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | -0.173910 | ... | -0.327520 | -0.333280 | -0.337580 | -0.341900 | -0.350560 | -0.354840 | -0.356520 | -0.356520 | -0.360870 | -0.365870 |
| 9 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | -0.169560 | ... | -0.173710 | -0.242660 | -0.256320 | -0.264790 | -0.269570 | -0.273910 | -0.282410 | -0.282610 | -0.282610 | -0.282610 |
10 rows × 60 columns
df_static_annotations_songs_1_2000
| workerID | SongId | Valence | Arousal | |
|---|---|---|---|---|
| 0 | 6010bbc8e7ef4b21fa38f9c3a9754ef3 | 2 | 5 | 2 |
| 1 | 3c888e77b992ae3cd2adfe16774e23b9 | 2 | 2 | 3 |
| 2 | 2afd218c3aecb6828d2be327f8b9c46f | 2 | 3 | 3 |
| 3 | fd5b08ce362d855ca9152a894348130c | 2 | 4 | 4 |
| 4 | 9c8073214a052e414811b76012df8847 | 2 | 2 | 2 |
| ... | ... | ... | ... | ... |
| 17459 | 607f6e34a0b5923333f6b16d3a59cc98 | 2000 | 6 | 5 |
| 17460 | 78b5e9744073532cc376976b5fc6b2fc | 2000 | 7 | 7 |
| 17461 | 7cecbffe1da5ae974952db6c13695afe | 2000 | 4 | 5 |
| 17462 | ed7ed76453bd846859f5e6b9149df276 | 2000 | 6 | 7 |
| 17463 | b09a5957e5d5e47e556d203529a0ae6d | 2000 | 6 | 7 |
17464 rows × 4 columns
df_static_2 = df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId']==2]
df_static_2
| workerID | SongId | Valence | Arousal | |
|---|---|---|---|---|
| 0 | 6010bbc8e7ef4b21fa38f9c3a9754ef3 | 2 | 5 | 2 |
| 1 | 3c888e77b992ae3cd2adfe16774e23b9 | 2 | 2 | 3 |
| 2 | 2afd218c3aecb6828d2be327f8b9c46f | 2 | 3 | 3 |
| 3 | fd5b08ce362d855ca9152a894348130c | 2 | 4 | 4 |
| 4 | 9c8073214a052e414811b76012df8847 | 2 | 2 | 2 |
| 5 | 6b492acb1838463dc7c07a76b26313c4 | 2 | 3 | 4 |
| 6 | 8dcd4de7be6479561269b90c1dc8a3b4 | 2 | 3 | 3 |
| 7 | cbc86e332e889251fe0fa71df472f202 | 2 | 4 | 3 |
| 8 | 259443e9cad56b4f68b664daa20cb323 | 2 | 2 | 3 |
| 9 | 5bf3c29fd496e2cafa9c531c7e9c6d61 | 2 | 3 | 3 |
#Посмотрим на распределение оценки valence для всех оценщиков
sns.boxplot(y=df_static_2[' Valence'])
<AxesSubplot:ylabel=' Valence'>
#Посмотрим на распределение оценки arousal для всех оценщиков
sns.boxplot(y=df_static_2[' Arousal'])
<AxesSubplot:ylabel=' Arousal'>
sns.boxplot(y=df_dynamic_valence_2.T.describe().T['mean'])
<AxesSubplot:ylabel='mean'>
sns.boxplot(y=df_dynamic_arousal_2.T.describe().T['mean'])
<AxesSubplot:ylabel='mean'>
#Сравним средние оценки динамические со статическими
#Нормализатор из 9-бальной шкалы в [-1,1]
def normilize(x):
return ((x-5)/4)
valence_2 = pd.DataFrame({"static": normilize(df_static_2[' Valence']), "dynamic": df_dynamic_valence_2.T.describe().T['mean']})
valence_2.plot(kind='box', title='boxplot')
<AxesSubplot:title={'center':'boxplot'}>
arousal_2 = pd.DataFrame({"static": normilize(df_static_2[' Arousal']), "dynamic": df_dynamic_arousal_2.T.describe().T['mean']})
arousal_2.plot(kind='box', title='boxplot')
<AxesSubplot:title={'center':'boxplot'}>
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=normilize(df_static_2[' Arousal']), y=normilize(df_static_2[' Valence']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=df_dynamic_arousal_2.T.describe().T['mean'], y=df_dynamic_valence_2.T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
#Посмотрим на данные для песни 1000.mp3
#датафреймы с размеченными данными для каждого оценщика для песни 1010.mp3
#df с динамическим измерением(за каждую секунду) arousal и valence
df_dynamic_arousal_1010 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/arousal/1010.csv')
df_dynamic_valence_1010 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/1010.csv')
df_dynamic_arousal_1010
| WorkerId | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_40000ms | sample_40500ms | sample_41000ms | sample_41500ms | sample_42000ms | sample_42500ms | sample_43000ms | sample_43500ms | sample_44000ms | sample_44500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 46a2289decf79f747406fa91cd92fc27 | 0.22 | 0.10 | -0.07 | -0.07 | -0.07 | -0.07 | -0.07 | -0.07 | -0.07 | ... | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 |
| 1 | 78b5e9744073532cc376976b5fc6b2fc | -0.10 | -0.06 | -0.02 | -0.00 | -0.00 | -0.00 | -0.00 | -0.00 | -0.00 | ... | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 | 0.47 |
| 2 | b09a5957e5d5e47e556d203529a0ae6d | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.07 | 0.08 | ... | 0.36 | 0.37 | 0.38 | 0.38 | 0.38 | 0.38 | 0.39 | 0.39 | 0.39 | 0.39 |
| 3 | 00de940f0b5cfc82cca4791199e3bfb3 | 0.32 | 0.32 | 0.32 | 0.32 | 0.32 | 0.32 | 0.32 | 0.32 | 0.32 | ... | 0.87 | 0.89 | 0.89 | 0.91 | 0.92 | 0.93 | 0.96 | 1.00 | 1.00 | 1.00 |
| 4 | accbf566ae920d6260d28454e1ee0d6a | -0.55 | -0.55 | -0.55 | -0.55 | -0.55 | -0.55 | -0.55 | -0.55 | -0.55 | ... | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.31 | 0.52 | 0.59 |
| 5 | de2b2c35312ac2f0a8510743742c0219 | 0.04 | 0.03 | 0.03 | 0.00 | 0.01 | 0.01 | 0.03 | 0.02 | -0.00 | ... | 0.90 | 0.90 | 0.92 | 0.95 | 0.96 | 0.92 | 0.95 | 0.91 | 0.88 | 0.94 |
| 6 | 607f6e34a0b5923333f6b16d3a59cc98 | 0.34 | 0.36 | 0.35 | 0.34 | 0.32 | 0.28 | 0.28 | 0.30 | 0.30 | ... | 0.45 | 0.47 | 0.44 | 0.45 | 0.46 | 0.41 | 0.50 | 0.49 | 0.50 | 0.48 |
| 7 | ff9c1993d2a21f2117c30d8e295dd4ac | -0.50 | -0.50 | -0.50 | -0.50 | -0.50 | -0.50 | -0.50 | -0.50 | -0.50 | ... | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 |
| 8 | ed7ed76453bd846859f5e6b9149df276 | 0.20 | 0.20 | 0.20 | 0.21 | 0.21 | 0.25 | 0.29 | 0.30 | 0.30 | ... | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 | 0.49 |
| 9 | a30d244141cb2f51e0803e79bc4bd147 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | ... | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 | 0.67 |
10 rows × 61 columns
df_dynamic_valence_1010
| WorkerId | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_40000ms | sample_40500ms | sample_41000ms | sample_41500ms | sample_42000ms | sample_42500ms | sample_43000ms | sample_43500ms | sample_44000ms | sample_44500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 46a2289decf79f747406fa91cd92fc27 | 0.11 | 0.11 | 0.12 | 0.14 | 0.15 | 0.15 | 0.15 | 0.15 | 0.15 | ... | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 | 0.24 |
| 1 | 78b5e9744073532cc376976b5fc6b2fc | 0.57 | 0.57 | 0.57 | 0.57 | 0.57 | 0.57 | 0.57 | 0.57 | 0.57 | ... | 0.57 | 0.55 | 0.46 | 0.46 | 0.46 | 0.46 | 0.46 | 0.46 | 0.47 | 0.56 |
| 2 | b09a5957e5d5e47e556d203529a0ae6d | 0.11 | 0.11 | 0.11 | 0.11 | 0.13 | 0.15 | 0.17 | 0.18 | 0.18 | ... | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.25 | 0.27 | 0.31 |
| 3 | 00de940f0b5cfc82cca4791199e3bfb3 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ... | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
| 4 | accbf566ae920d6260d28454e1ee0d6a | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | 0.43 | ... | 0.66 | 0.66 | 0.66 | 0.55 | 0.55 | 0.55 | 0.59 | 0.63 | 0.63 | 0.75 |
| 5 | de2b2c35312ac2f0a8510743742c0219 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.01 | 0.03 | ... | 0.92 | 0.94 | 0.94 | 0.92 | 0.93 | 0.92 | 0.91 | 0.95 | 0.93 | 0.92 |
| 6 | 607f6e34a0b5923333f6b16d3a59cc98 | 0.26 | 0.26 | 0.27 | 0.28 | 0.28 | 0.28 | 0.28 | 0.29 | 0.28 | ... | 0.41 | 0.41 | 0.41 | 0.38 | 0.43 | 0.39 | 0.49 | 0.46 | 0.47 | 0.44 |
| 7 | ff9c1993d2a21f2117c30d8e295dd4ac | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | -0.02 | ... | -0.33 | -0.33 | -0.33 | -0.33 | -0.33 | -0.33 | -0.33 | -0.33 | -0.33 | -0.33 |
| 8 | ed7ed76453bd846859f5e6b9149df276 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | 0.48 | ... | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 | 0.53 |
| 9 | a30d244141cb2f51e0803e79bc4bd147 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | ... | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 | 0.69 |
10 rows × 61 columns
df_static_1010 = df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId']==1010]
df_static_1010
| workerID | SongId | Valence | Arousal | |
|---|---|---|---|---|
| 7554 | 46a2289decf79f747406fa91cd92fc27 | 1010 | 5 | 5 |
| 7555 | 78b5e9744073532cc376976b5fc6b2fc | 1010 | 7 | 7 |
| 7556 | b09a5957e5d5e47e556d203529a0ae6d | 1010 | 6 | 6 |
| 7557 | 00de940f0b5cfc82cca4791199e3bfb3 | 1010 | 9 | 9 |
| 7558 | accbf566ae920d6260d28454e1ee0d6a | 1010 | 8 | 7 |
| 7559 | de2b2c35312ac2f0a8510743742c0219 | 1010 | 9 | 9 |
| 7560 | 607f6e34a0b5923333f6b16d3a59cc98 | 1010 | 6 | 5 |
| 7561 | ff9c1993d2a21f2117c30d8e295dd4ac | 1010 | 4 | 5 |
| 7562 | ed7ed76453bd846859f5e6b9149df276 | 1010 | 5 | 5 |
| 7563 | a30d244141cb2f51e0803e79bc4bd147 | 1010 | 6 | 7 |
#Посмотрим на распределение оценки valence для всех оценщиков
sns.boxplot(y=df_static_1010[' Valence'])
<AxesSubplot:ylabel=' Valence'>
#Посмотрим на распределение оценки arousal для всех оценщиков
sns.boxplot(y=df_static_1010[' Arousal'])
<AxesSubplot:ylabel=' Arousal'>
sns.boxplot(y=df_dynamic_valence_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'])
<AxesSubplot:ylabel='mean'>
sns.boxplot(y=df_dynamic_arousal_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'])
<AxesSubplot:ylabel='mean'>
valence_1010 = pd.DataFrame({"static": normilize(df_static_1010[' Valence']), "dynamic": df_dynamic_valence_1010.drop(['WorkerId'],axis=1).T.describe().T['mean']})
valence_1010.plot(kind='box', title='boxplot')
<AxesSubplot:title={'center':'boxplot'}>
arousal_1010 = pd.DataFrame({"static": normilize(df_static_1010[' Arousal']), "dynamic": df_dynamic_arousal_1010.drop(['WorkerId'],axis=1).T.describe().T['mean']})
arousal_1010.plot(kind='box', title='boxplot')
<AxesSubplot:title={'center':'boxplot'}>
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=normilize(df_static_1010[' Arousal']), y=normilize(df_static_1010[' Valence']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(x=df_dynamic_arousal_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'], y=df_dynamic_valence_1010.drop(['WorkerId'],axis=1).T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
def find_outliers(df):
q1=df.quantile(0.25)
q3=df.quantile(0.75)
IQR=q3-q1
outliers = df[((df<(q1-1.5*IQR)) | (df>(q3+1.5*IQR)))]
return outliers
#количество оценщиков для 2013-2014 статических измерений
len(df_static_annotations_songs_1_2000['workerID'].unique())
187
#подсчёт количества выбросов для каждого работника и для каждого статического измерения
#словарь, содержащий количество выбросов в arousal для определенного работника
dict_arousal_outliers_s = {}
#словарь, содержащий количество выбросов в valence для определенного работника
dict_valence_outliers_s = {}
dict_arousal_outliers_s = dict_arousal_outliers_s.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
dict_valence_outliers_s = dict_valence_outliers_s.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
df_metadata_2013 = pd.read_csv('audio_data/metadata/metadata_2013.csv')
df_metadata_2014 = pd.read_csv('audio_data/metadata/metadata_2014.csv', usecols=['Id','Artist','Album','Track','Genre','segment start','segment end','last.fm labels'])
for i in df_metadata_2014['Id']:
outliers_arousal_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Arousal'])
for j in outliers_arousal_s.index:
dict_arousal_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1
outliers_valence_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Valence'])
for j in outliers_valence_s.index:
dict_valence_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1
print(dict_arousal_outliers_s,'\n')
print(dict_valence_outliers_s,'\n')
dict_arousal_outliers_s_2014 = dict_arousal_outliers_s.copy()
dict_valence_outliers_s_2014 = dict_valence_outliers_s.copy()
for i in df_metadata_2013['song_id']:
outliers_arousal_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Arousal'])
for j in outliers_arousal_s.index:
dict_arousal_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1
outliers_valence_s = find_outliers(df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i][' Valence'])
for j in outliers_valence_s.index:
dict_valence_outliers_s[df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == i].loc[j]['workerID']]+=1
print(dict_arousal_outliers_s,'\n')
print(dict_valence_outliers_s)
{'6010bbc8e7ef4b21fa38f9c3a9754ef3': 0, '3c888e77b992ae3cd2adfe16774e23b9': 0, '2afd218c3aecb6828d2be327f8b9c46f': 0, 'fd5b08ce362d855ca9152a894348130c': 0, '9c8073214a052e414811b76012df8847': 0, '6b492acb1838463dc7c07a76b26313c4': 0, '8dcd4de7be6479561269b90c1dc8a3b4': 0, 'cbc86e332e889251fe0fa71df472f202': 0, '259443e9cad56b4f68b664daa20cb323': 0, '5bf3c29fd496e2cafa9c531c7e9c6d61': 0, 'bf34e3c5724ce07d29ef12db5f767258': 0, '514278711fcdb6dfb235c03a3aa38ee3': 0, '4eb43aa7f89260bb40b3970a45a82678': 0, 'ed7ed76453bd846859f5e6b9149df276': 18, '571d66ea4abfb694d9bb1281d7701710': 0, 'd84d58533b58797bccca5811b4135c95': 0, '490951556961e4b88fe1a3ac53b3b186': 0, '2c5952c4f931410e9010eb87c276020d': 0, '0c37993dcf5f99f7b00d02495ed01bc3': 0, '64cae7b86c7dcb1b40d17e43c0c2109e': 0, '24ebcf86498b0f2793b55c5b9a7756b5': 0, '94d7a0f7c8f2e1c714b0649f835c538c': 0, 'a00de41fdf121f6dfe5db851a575d2d1': 0, '7cecbffe1da5ae974952db6c13695afe': 19, 'eca1130f44cd2e17645e40a0fa2ef59b': 12, 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'93f4b8995f647ceb58689b78eb2087ef': 4, '186575d01f40537515988f1369d395eb': 2, 'd0520656367db74cb316b9a26f60864e': 1, '55016bfc877fc509861b4aba22ee4f7a': 0, 'f71b423642d3d61dffba53ddee48a7e0': 2, 'd4d0cd1ae06f54909291d34ddd6ab497': 2, 'a9f376da22eb77185afae90b192bf9ac': 1, '65794ea9f5122952403585a237bc5e52': 0, '1eb9e2c62709ff203b2d9a558abc0937': 3, 'd633894e43380403a7984858af42ecea': 2, '26bf37eb1750d163fe01ef21b0033a77': 0, '51452faff8948ad4780f9b6261e813a7': 2, 'c182e8b2f013e4e3c78cbfb01b8970c8': 2, '5862049dbaad1750964d91a98930832f': 3, '6db6afec230a8c59397b5f4d97f5da28': 0, '5bdea9864567a8614b659bf4b2132ab0': 3, '0bda2743e12d8a204ab04122658d73a3': 0, '74f10ac255db9e37db6c6cbf604eb7e0': 1, '9c3b8737a6a00626867c5e05ee80157e': 0, 'e9fda1f240982b20e0df5ba9c4a337e9': 0, '888b76dff350bdb6b25783d7c4110081': 1, 'a2a845665afabbc319eb7c5305b0a7ec': 0, '2461c19378deafd4ec26d3993eeef635': 3, 'c19c053aafbf1492b9e57ce3da5d45c1': 0, 'ca76c553e32c2f096c3bc8f7280ba408': 0, 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'80db3788bc598d1b32979bea958d9358': 0, '4dedf223de3f8ebde100df78a5428251': 0, '2a6b63b7690efa2390c8d9fee11b1407': 0, '485d8e33a731a830ef0aebd71b016d08': 0, 'b92704ea431d60d62bd8fd18e2940067': 0, 'a4410f548a05bf09b8541ec2672d8490': 0, '67785f343e6d117fac4d1697697cd8ae': 0, 'da37d1548ffd0631809f7be341e4fe4d': 0, 'ad3b997c4f2382a66e49f035cacfa682': 0, 'a34913ea1010b5812a14d1fef9586a4f': 0, '659accfd85162122ca361ac34e730c4d': 0, '9d32be9708a9520ec07e91aec9653291': 0}
#песня, которую оценил 1 работник 2 раза
df_static_annotations_songs_1_2000[df_static_annotations_songs_1_2000[' SongId'] == 323]
| workerID | SongId | Valence | Arousal | |
|---|---|---|---|---|
| 2730 | 6010bbc8e7ef4b21fa38f9c3a9754ef3 | 323 | 7 | 6 |
| 2731 | bf34e3c5724ce07d29ef12db5f767258 | 323 | 4 | 6 |
| 2732 | 24ebcf86498b0f2793b55c5b9a7756b5 | 323 | 5 | 4 |
| 2733 | 38531641e6c0628757776b0088bcc854 | 323 | 6 | 6 |
| 2734 | 2afd218c3aecb6828d2be327f8b9c46f | 323 | 3 | 5 |
| 2735 | fd5b08ce362d855ca9152a894348130c | 323 | 6 | 4 |
| 2736 | 9c8073214a052e414811b76012df8847 | 323 | 6 | 7 |
| 2737 | 64cae7b86c7dcb1b40d17e43c0c2109e | 323 | 3 | 3 |
| 2738 | 64cae7b86c7dcb1b40d17e43c0c2109e | 323 | 4 | 8 |
| 2739 | 259443e9cad56b4f68b664daa20cb323 | 323 | 3 | 7 |
| 2740 | 490951556961e4b88fe1a3ac53b3b186 | 323 | 6 | 7 |
#подсчёт количества выбросов для каждого работника и для каждого среднего динамического измерения
#словарь, содержащий количество выбросов в arousal для определенного работника
dict_arousal_outliers_d = {}
dict_arousal_outliers_d = dict_arousal_outliers_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
#словарь, содержащий количество выбросов в valence для определенного работника
dict_valence_outliers_d = {}
dict_valence_outliers_d = dict_valence_outliers_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
directory = 'audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)'
#в 2013 не собиралась информация по id работникам для динамических измерений
for i in df_metadata_2014['Id']:
df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
if ('WorkerId' in df_arousal_d.columns):
#print(i)
#print(df_arousal_d)
index_series = df_arousal_d['WorkerId']
df_arousal_d_mean = df_arousal_d.drop('WorkerId', axis=1).T.describe().T['mean']
#print(df_arousal_d_mean)
#print(index_series)
df_arousal_d_mean = df_arousal_d_mean.set_axis(index_series)
#print(df_arousal_d_mean)
######
outliers_arousal_d = find_outliers(df_arousal_d_mean)
#print(outliers_arousal_d)
for j in outliers_arousal_d.index:
dict_arousal_outliers_d[j]+=1
df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
if ('WorkerId' in df_valence_d.columns):
#print(i)
#print(df_valence_d)
df_valence_d_mean = df_valence_d.drop('WorkerId', axis=1).T.describe().T['mean']
index_series = df_valence_d['WorkerId']
#print(df_valence_d_mean)
#print(index_series)
df_valence_d_mean = df_valence_d_mean.set_axis(index_series)
#print(df_valence_d_mean)
#####
outliers_valence_d = find_outliers(df_valence_d_mean)
#print(outliers_valence_d)
for j in outliers_valence_d.index:
dict_valence_outliers_d[j]+=1
print(dict_arousal_outliers_d,'\n')
print(dict_valence_outliers_d)
{'6010bbc8e7ef4b21fa38f9c3a9754ef3': 0, '3c888e77b992ae3cd2adfe16774e23b9': 0, '2afd218c3aecb6828d2be327f8b9c46f': 0, 'fd5b08ce362d855ca9152a894348130c': 0, '9c8073214a052e414811b76012df8847': 0, '6b492acb1838463dc7c07a76b26313c4': 0, '8dcd4de7be6479561269b90c1dc8a3b4': 0, 'cbc86e332e889251fe0fa71df472f202': 0, '259443e9cad56b4f68b664daa20cb323': 0, '5bf3c29fd496e2cafa9c531c7e9c6d61': 0, 'bf34e3c5724ce07d29ef12db5f767258': 0, '514278711fcdb6dfb235c03a3aa38ee3': 0, '4eb43aa7f89260bb40b3970a45a82678': 0, 'ed7ed76453bd846859f5e6b9149df276': 7, '571d66ea4abfb694d9bb1281d7701710': 0, 'd84d58533b58797bccca5811b4135c95': 0, '490951556961e4b88fe1a3ac53b3b186': 0, '2c5952c4f931410e9010eb87c276020d': 0, '0c37993dcf5f99f7b00d02495ed01bc3': 0, '64cae7b86c7dcb1b40d17e43c0c2109e': 0, '24ebcf86498b0f2793b55c5b9a7756b5': 0, '94d7a0f7c8f2e1c714b0649f835c538c': 0, 'a00de41fdf121f6dfe5db851a575d2d1': 0, '7cecbffe1da5ae974952db6c13695afe': 24, 'eca1130f44cd2e17645e40a0fa2ef59b': 32, 'd8e56509dc582df255732a0170323231': 0, 'de0b07c32bb6ca4655487026e0f3fc38': 0, '3226bc99212ad30ce2ced981de1c437a': 0, '5097e48e561bf0261b242f211b289825': 0, '9d1aa48692579ca9330d3c61d10e22e6': 0, 'ce209fb2fe01d6c85dbb29b6fd84b563': 0, '8eb1abd1acca601d1e23e85c69b1742a': 0, '7a81cab11e4b2b4aa9c0718c90835611': 0, '6ca7c27715f54e2f47ebe4e1c9dcf842': 0, 'f0e2dfdbc324f309660b731ad16ce2fd': 0, '00fa8d61b5f75e16ea71bff7a3def9c9': 0, '7685fa48defdc384f720bd1edbf17ed6': 0, 'c1edc56f4d30d94a94ee41654c4f6734': 0, 'a2a15d03117e137478c5718212805270': 0, '9ad2a06dfac81761a123cb40084dcbd7': 0, '28b56a449c99c88e1f9fff59d0f16aac': 0, 'c795c7de8f350ab35dfd1100a84bf213': 0, 'f8ecfe868f673b16e2f6e5614cb46b7d': 0, '4b4c4f319a356ea87affb872173a2095': 0, '20c7d1ee0615a09b0bac9438cc8325e2': 0, '861d48b913f37227fcf9a7b1e547c0bb': 0, 'be4e771b30940fb2d5f5dcfe3877dd2e': 0, '4480a650a96730fb629aa7fa2079e7c3': 0, 'a0f5cedc3a2371ec13663226c4b44771': 0, 'cd09cd99c38d4bfb7b554dff21196c22': 0, '1ee4bd59c9ea084016ff2811dbb00568': 0, '145b408fa103c16bb00e8734cc39a29f': 0, 'be6ec863f38f611dff4958ccd33cf638': 0, '7a23690289260d2b8c1d71d8c22b69f0': 0, '3bf93d5f7b82b46e38feb881d00c5798': 0, '1bd2dc9e0406a2e240b288685c87fa76': 0, '212246528da7c13457844fc4453e6019': 0, '84c2d6fc2e78a1d1283c386c0d4daee4': 0, 'e347f98d0a810ded7c14d7be813a5868': 0, '04db9e73677c3b8882908ef7ee4a3424': 0, 'ff18a27328ffd40ef52b7ebb7a0ded94': 0, '3c63524f15931dc8bed7a768cb7d6150': 0, '807dfc125f09be392a1f1b58c05b3742': 0, '4dfca2f4dc4e8e6c3b1b7d32e10579c1': 0, '3deba1aaf448d3857c6c70f63c2a6430': 0, '855ba3170f3e470170d23a748ccb41ed': 0, 'dd80f1940fe5b84eddde6e83542bd89b': 0, '2ec24376127909f3be36d770e6ae41a6': 0, 'b8ef6a913a63225faafd661ee2e1a7c0': 0, '34fb7d43f3a35d4afac7563b7453e31c': 0, 'f3008c23ea8f35ef870e735df67fcc46': 0, '5fa91ab06a079c59a1d9e8174a488f91': 0, '504b651e27c89d1d46d101dd87823ab6': 0, '38531641e6c0628757776b0088bcc854': 0, '807f0025a626896f04566aa37cfbce0d': 0, 'd0c51e42ea093dc9a9a98ef888637c8e': 0, 'ccfcf36a939a8af15a987fa562a49207': 0, '7975199043cec980e587889f1639c0a4': 0, 'b13fb0da6dd2f5d7fe398e6e53b52110': 0, '15ec33e862185406170ff931583b014f': 0, 'c3c21239b85dcdd6679fc212afd02a49': 0, '478905563bab736f35d94bd7b0a27f9a': 0, '2f790705ae66e70e81cc0f11ce0f4b9b': 0, '27f51a4a7fe8565d26cadb88584441e5': 0, '52b597296a8dca0c5cc5a19231eeff89': 0, '1065a2110392d3e83d61b4282f55353c': 0, '6d59196a3f495939a1a776cbebe212ba': 0, 'fa3305c1bc04047d36a8b7a4c77fa81b': 0, 'fecd63ea857606ed61cbfe2ba1a70b3b': 0, '51f5fb156a5c0051682ee1835e30fbbd': 0, 'd88c800327bffffea5562e23c276ede3': 0, '594f3fb0d194f109697627b5241f4011': 0, 'a186cdd58a92051b7c73adc9bd6e65ca': 0, '40b86bbb6927a793c05968b8b95e214d': 0, 'b37092cf23b42f8b8497d8ba89be157a': 0, '3111e02887b600ee085c72c0a3df33e8': 0, 'd8188e46705e2e87f6a051e0cb338dea': 0, '19fee46f2810f34a8b69a7768d897a59': 0, '56a384c7fb2545f9de3e85fa7445d92b': 0, 'e105c200f413d7b2c5850c0df4b9687e': 0, 'a30d244141cb2f51e0803e79bc4bd147': 69, '8ef6c1b6e31d907a3bba6f4edd303371': 0, '610d5d89f56667e3d4537c529aae496e': 1, '78b5e9744073532cc376976b5fc6b2fc': 7, 'de2b2c35312ac2f0a8510743742c0219': 66, '607f6e34a0b5923333f6b16d3a59cc98': 14, 'bb50b45a1874ede476874bd57e4cabb4': 1, 'ff9c1993d2a21f2117c30d8e295dd4ac': 138, 'b09a5957e5d5e47e556d203529a0ae6d': 28, '00de940f0b5cfc82cca4791199e3bfb3': 13, '623681f76a3eab5d9c86fbc0e1ca264b': 0, '11e5c2ba0236fe3643cac09dbdb96580': 1, '722ad4c331b7d7bebd697bfbb91ee0e8': 3, '2db8293d2a35a17f16ddf8e97122ec11': 2, 'b36ffffcd1831441a2a3b60919312ccd': 7, '6222da90667e5b0de990ce6c26dcfa15': 1, '883449004b1bf2a07a284f59ddae1fd7': 3, 'accbf566ae920d6260d28454e1ee0d6a': 5, '4c7a94e0937450ef406135849493ab99': 0, '2fb527b567d10a3ea0dac783c7c2f364': 1, '1ac10b86ba5ac18e06eea9f9954ab216': 4, '46a2289decf79f747406fa91cd92fc27': 5, '0ca907e233b8bf33bf4eca86fcbfcc8f': 0, 'd5cf45435e35cb9166f51f0a55b74a0e': 4, '62ef0e23f21039e337b545ccc4851525': 3, '93f4b8995f647ceb58689b78eb2087ef': 4, '186575d01f40537515988f1369d395eb': 0, 'd0520656367db74cb316b9a26f60864e': 1, '55016bfc877fc509861b4aba22ee4f7a': 0, 'f71b423642d3d61dffba53ddee48a7e0': 0, 'd4d0cd1ae06f54909291d34ddd6ab497': 5, 'a9f376da22eb77185afae90b192bf9ac': 0, '65794ea9f5122952403585a237bc5e52': 0, '1eb9e2c62709ff203b2d9a558abc0937': 0, 'd633894e43380403a7984858af42ecea': 1, '26bf37eb1750d163fe01ef21b0033a77': 0, '51452faff8948ad4780f9b6261e813a7': 1, 'c182e8b2f013e4e3c78cbfb01b8970c8': 2, '5862049dbaad1750964d91a98930832f': 0, '6db6afec230a8c59397b5f4d97f5da28': 3, '5bdea9864567a8614b659bf4b2132ab0': 1, '0bda2743e12d8a204ab04122658d73a3': 0, '74f10ac255db9e37db6c6cbf604eb7e0': 0, '9c3b8737a6a00626867c5e05ee80157e': 0, 'e9fda1f240982b20e0df5ba9c4a337e9': 1, '888b76dff350bdb6b25783d7c4110081': 0, 'a2a845665afabbc319eb7c5305b0a7ec': 0, '2461c19378deafd4ec26d3993eeef635': 1, 'c19c053aafbf1492b9e57ce3da5d45c1': 0, 'ca76c553e32c2f096c3bc8f7280ba408': 0, '027cefa6afc040448d29558b3175cdc1': 0, 'e336e40ae9ac101cec57e8e4323ffb79': 0, '4c2037269ef8fd0a3349e6b54dd1bb1f': 0, '248172d9efbbd5eb72d48de6884054ab': 0, 'f7502ffb3616a789e3e5b38f7325b95f': 0, '3ca50399775f0540bd002cb0d6f54a3d': 0, 'ba85e679d3c3ac42412cb8deaba66a5a': 0, '49be5653eaba26f1eb60fd9d63f23502': 0, '9edfd17e8b78d0981868f84c5f20a118': 0, '6cb1856680ea078c9f46437e2bdcd09e': 1, '651938620e6e6c78bfa7854784fe62c2': 0, 'fc2fa5656d42b49f3caf01f663085069': 0, '615d836ba25132081e0ebd2182221a59': 0, 'b6927bdee51077f7868f05f1ed39485f': 1, 'dbb8ffe292aaa9bc2de69ca511af2b3a': 0, '54cc66fe7cbb01775a6b7c7d703cdeff': 0, 'b4ba75169c585b959f1247fac7e4be7a': 0, 'd3b1de8ed2ffc3eb9af2ef40a80c7d7d': 0, '0aef6e22005035ac1b93e0a99c961f4c': 3, 'd5b7242bc8a4bb534b8489c24b81fc34': 0, '374a5659c02e12b01db6319436f17a7d': 0, '5b044cf509da1d8444b6f60c465240ef': 0, '77055e16055dc7bc817f7c4bb2eb8fae': 0, '35536d2d90b93461aa74200967d002e8': 0, '987a16808cac2496853b6f531c0878cb': 0, '80db3788bc598d1b32979bea958d9358': 0, '4dedf223de3f8ebde100df78a5428251': 0, '2a6b63b7690efa2390c8d9fee11b1407': 0, '485d8e33a731a830ef0aebd71b016d08': 0, 'b92704ea431d60d62bd8fd18e2940067': 0, 'a4410f548a05bf09b8541ec2672d8490': 0, '67785f343e6d117fac4d1697697cd8ae': 0, 'da37d1548ffd0631809f7be341e4fe4d': 0, 'ad3b997c4f2382a66e49f035cacfa682': 0, 'a34913ea1010b5812a14d1fef9586a4f': 0, '659accfd85162122ca361ac34e730c4d': 0, '9d32be9708a9520ec07e91aec9653291': 1}
{'6010bbc8e7ef4b21fa38f9c3a9754ef3': 0, '3c888e77b992ae3cd2adfe16774e23b9': 0, '2afd218c3aecb6828d2be327f8b9c46f': 0, 'fd5b08ce362d855ca9152a894348130c': 0, '9c8073214a052e414811b76012df8847': 0, '6b492acb1838463dc7c07a76b26313c4': 0, '8dcd4de7be6479561269b90c1dc8a3b4': 0, 'cbc86e332e889251fe0fa71df472f202': 0, '259443e9cad56b4f68b664daa20cb323': 0, '5bf3c29fd496e2cafa9c531c7e9c6d61': 0, 'bf34e3c5724ce07d29ef12db5f767258': 0, '514278711fcdb6dfb235c03a3aa38ee3': 0, '4eb43aa7f89260bb40b3970a45a82678': 0, 'ed7ed76453bd846859f5e6b9149df276': 7, '571d66ea4abfb694d9bb1281d7701710': 0, 'd84d58533b58797bccca5811b4135c95': 0, '490951556961e4b88fe1a3ac53b3b186': 0, '2c5952c4f931410e9010eb87c276020d': 0, '0c37993dcf5f99f7b00d02495ed01bc3': 0, '64cae7b86c7dcb1b40d17e43c0c2109e': 0, '24ebcf86498b0f2793b55c5b9a7756b5': 0, '94d7a0f7c8f2e1c714b0649f835c538c': 0, 'a00de41fdf121f6dfe5db851a575d2d1': 0, '7cecbffe1da5ae974952db6c13695afe': 19, 'eca1130f44cd2e17645e40a0fa2ef59b': 26, 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'1ee4bd59c9ea084016ff2811dbb00568': 0, '145b408fa103c16bb00e8734cc39a29f': 0, 'be6ec863f38f611dff4958ccd33cf638': 1, '7a23690289260d2b8c1d71d8c22b69f0': 0, '3bf93d5f7b82b46e38feb881d00c5798': 0, '1bd2dc9e0406a2e240b288685c87fa76': 0, '212246528da7c13457844fc4453e6019': 0, '84c2d6fc2e78a1d1283c386c0d4daee4': 0, 'e347f98d0a810ded7c14d7be813a5868': 0, '04db9e73677c3b8882908ef7ee4a3424': 0, 'ff18a27328ffd40ef52b7ebb7a0ded94': 0, '3c63524f15931dc8bed7a768cb7d6150': 0, '807dfc125f09be392a1f1b58c05b3742': 0, '4dfca2f4dc4e8e6c3b1b7d32e10579c1': 0, '3deba1aaf448d3857c6c70f63c2a6430': 0, '855ba3170f3e470170d23a748ccb41ed': 0, 'dd80f1940fe5b84eddde6e83542bd89b': 0, '2ec24376127909f3be36d770e6ae41a6': 0, 'b8ef6a913a63225faafd661ee2e1a7c0': 0, '34fb7d43f3a35d4afac7563b7453e31c': 0, 'f3008c23ea8f35ef870e735df67fcc46': 0, '5fa91ab06a079c59a1d9e8174a488f91': 0, '504b651e27c89d1d46d101dd87823ab6': 0, '38531641e6c0628757776b0088bcc854': 0, '807f0025a626896f04566aa37cfbce0d': 0, 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'a30d244141cb2f51e0803e79bc4bd147': 39, '8ef6c1b6e31d907a3bba6f4edd303371': 0, '610d5d89f56667e3d4537c529aae496e': 0, '78b5e9744073532cc376976b5fc6b2fc': 3, 'de2b2c35312ac2f0a8510743742c0219': 68, '607f6e34a0b5923333f6b16d3a59cc98': 22, 'bb50b45a1874ede476874bd57e4cabb4': 9, 'ff9c1993d2a21f2117c30d8e295dd4ac': 83, 'b09a5957e5d5e47e556d203529a0ae6d': 29, '00de940f0b5cfc82cca4791199e3bfb3': 8, '623681f76a3eab5d9c86fbc0e1ca264b': 0, '11e5c2ba0236fe3643cac09dbdb96580': 0, '722ad4c331b7d7bebd697bfbb91ee0e8': 1, '2db8293d2a35a17f16ddf8e97122ec11': 7, 'b36ffffcd1831441a2a3b60919312ccd': 6, '6222da90667e5b0de990ce6c26dcfa15': 0, '883449004b1bf2a07a284f59ddae1fd7': 12, 'accbf566ae920d6260d28454e1ee0d6a': 12, '4c7a94e0937450ef406135849493ab99': 0, '2fb527b567d10a3ea0dac783c7c2f364': 0, '1ac10b86ba5ac18e06eea9f9954ab216': 8, '46a2289decf79f747406fa91cd92fc27': 5, '0ca907e233b8bf33bf4eca86fcbfcc8f': 1, 'd5cf45435e35cb9166f51f0a55b74a0e': 3, '62ef0e23f21039e337b545ccc4851525': 0, '93f4b8995f647ceb58689b78eb2087ef': 2, '186575d01f40537515988f1369d395eb': 0, 'd0520656367db74cb316b9a26f60864e': 0, '55016bfc877fc509861b4aba22ee4f7a': 0, 'f71b423642d3d61dffba53ddee48a7e0': 0, 'd4d0cd1ae06f54909291d34ddd6ab497': 2, 'a9f376da22eb77185afae90b192bf9ac': 0, '65794ea9f5122952403585a237bc5e52': 0, '1eb9e2c62709ff203b2d9a558abc0937': 1, 'd633894e43380403a7984858af42ecea': 0, '26bf37eb1750d163fe01ef21b0033a77': 0, '51452faff8948ad4780f9b6261e813a7': 1, 'c182e8b2f013e4e3c78cbfb01b8970c8': 1, '5862049dbaad1750964d91a98930832f': 0, '6db6afec230a8c59397b5f4d97f5da28': 2, '5bdea9864567a8614b659bf4b2132ab0': 7, '0bda2743e12d8a204ab04122658d73a3': 0, '74f10ac255db9e37db6c6cbf604eb7e0': 0, '9c3b8737a6a00626867c5e05ee80157e': 0, 'e9fda1f240982b20e0df5ba9c4a337e9': 0, '888b76dff350bdb6b25783d7c4110081': 0, 'a2a845665afabbc319eb7c5305b0a7ec': 1, '2461c19378deafd4ec26d3993eeef635': 0, 'c19c053aafbf1492b9e57ce3da5d45c1': 2, 'ca76c553e32c2f096c3bc8f7280ba408': 0, '027cefa6afc040448d29558b3175cdc1': 1, 'e336e40ae9ac101cec57e8e4323ffb79': 0, '4c2037269ef8fd0a3349e6b54dd1bb1f': 0, '248172d9efbbd5eb72d48de6884054ab': 0, 'f7502ffb3616a789e3e5b38f7325b95f': 0, '3ca50399775f0540bd002cb0d6f54a3d': 0, 'ba85e679d3c3ac42412cb8deaba66a5a': 0, '49be5653eaba26f1eb60fd9d63f23502': 0, '9edfd17e8b78d0981868f84c5f20a118': 0, '6cb1856680ea078c9f46437e2bdcd09e': 0, '651938620e6e6c78bfa7854784fe62c2': 0, 'fc2fa5656d42b49f3caf01f663085069': 0, '615d836ba25132081e0ebd2182221a59': 0, 'b6927bdee51077f7868f05f1ed39485f': 0, 'dbb8ffe292aaa9bc2de69ca511af2b3a': 1, '54cc66fe7cbb01775a6b7c7d703cdeff': 0, 'b4ba75169c585b959f1247fac7e4be7a': 0, 'd3b1de8ed2ffc3eb9af2ef40a80c7d7d': 0, '0aef6e22005035ac1b93e0a99c961f4c': 0, 'd5b7242bc8a4bb534b8489c24b81fc34': 0, '374a5659c02e12b01db6319436f17a7d': 0, '5b044cf509da1d8444b6f60c465240ef': 0, '77055e16055dc7bc817f7c4bb2eb8fae': 0, '35536d2d90b93461aa74200967d002e8': 0, '987a16808cac2496853b6f531c0878cb': 0, '80db3788bc598d1b32979bea958d9358': 0, '4dedf223de3f8ebde100df78a5428251': 0, '2a6b63b7690efa2390c8d9fee11b1407': 0, '485d8e33a731a830ef0aebd71b016d08': 0, 'b92704ea431d60d62bd8fd18e2940067': 1, 'a4410f548a05bf09b8541ec2672d8490': 0, '67785f343e6d117fac4d1697697cd8ae': 0, 'da37d1548ffd0631809f7be341e4fe4d': 0, 'ad3b997c4f2382a66e49f035cacfa682': 0, 'a34913ea1010b5812a14d1fef9586a4f': 1, '659accfd85162122ca361ac34e730c4d': 0, '9d32be9708a9520ec07e91aec9653291': 0}
#сравнение количества выбросов в arousal в статике и динамике в 2014 году
plt.bar(['static_arousal_2014','dynamic_arousal_2014'], [sum(dict_arousal_outliers_s_2014.values()),sum(dict_arousal_outliers_d.values())])
plt.ylabel('количество выбросов')
plt.show()
#сравнение количества выбросов в valence в статике и динамике в 2014 году
plt.bar(['static_valence_2014','dynamic_valence_2014'], [sum(dict_valence_outliers_s_2014.values()),sum(dict_valence_outliers_d.values())])
plt.ylabel('количество выбросов')
plt.show()
#топ-10 работников по количеству выбросов в static_valence
print('Количество работников всего =', len(dict_valence_outliers_s))
print('Количество работников с 0 outlier =', len([k for k, v in dict_valence_outliers_s.items() if v==0]))
print([i[1] for i in sorted(dict_valence_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_valence_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_valence_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()
Количество работников всего = 187 Количество работников с 0 outlier = 91 [182, 108, 35, 34, 31, 28, 26, 24, 24, 20]
#топ-10 работников по количеству выбросов в static_arousal
print('Количество работников всего =', len(dict_arousal_outliers_s))
print('Количество работников с 0 outlier =', len([k for k, v in dict_arousal_outliers_s.items() if v==0]))
print([i[1] for i in sorted(dict_arousal_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_arousal_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_arousal_outliers_s.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()
Количество работников всего = 187 Количество работников с 0 outlier = 99 [136, 107, 74, 53, 40, 33, 33, 30, 29, 27]
#топ-10 работников по количеству выбросов в dynamic_valence
print('Количество работников всего =', len(dict_valence_outliers_d))
print('Количество работников с 0 outlier =', len([k for k, v in dict_valence_outliers_d.items() if v==0]))
print([i[1] for i in sorted(dict_valence_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_valence_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_valence_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()
Количество работников всего = 187 Количество работников с 0 outlier = 153 [83, 68, 39, 29, 26, 22, 19, 12, 12, 9]
#топ-10 работников по количеству выбросов в dynamic_arousal
print('Количество работников всего =', len(dict_arousal_outliers_d))
print('Количество работников с 0 outlier =', len([k for k, v in dict_arousal_outliers_d.items() if v==0]))
print([i[1] for i in sorted(dict_arousal_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]])
plt.pie([i[1] for i in sorted(dict_arousal_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],labels=[i[0] for i in sorted(dict_arousal_outliers_d.items(), key=lambda item: item[1], reverse=True)[:10]],autopct= '%1.1f%%')
plt.show()
Количество работников всего = 187 Количество работников с 0 outlier = 149 [138, 69, 66, 32, 28, 24, 14, 13, 7, 7]
#Диаграмма, содержащая долю выбросов в данных 2014 года на каждого работника
fig, ax = plt.subplots(figsize=(11,11))
dict_all_outliers = {k : dict_arousal_outliers_d.get(k,0)+dict_arousal_outliers_s.get(k,0)+dict_valence_outliers_d.get(k,0)+dict_valence_outliers_s.get(k,0) for k in set(dict_valence_outliers_s)|set(dict_valence_outliers_d)|set(dict_arousal_outliers_s)|set(dict_arousal_outliers_d)}
print([i[1] for i in sorted(dict_all_outliers.items(), key=lambda item: item[1], reverse=True)])
plt.pie([i[1] for i in sorted(dict_all_outliers.items(), key=lambda item: item[1], reverse=True)],labels=[i[0] for i in sorted(dict_all_outliers.items(), key=lambda item: item[1], reverse=True)],autopct=lambda p: format(p, '.2f') if p > 2.5 else None)
plt.show()
[423, 253, 244, 210, 108, 102, 89, 87, 79, 75, 70, 51, 45, 40, 39, 37, 33, 32, 32, 31, 30, 26, 26, 26, 23, 22, 20, 18, 17, 17, 16, 14, 13, 11, 10, 10, 10, 10, 9, 9, 8, 8, 7, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
#данные 2015 статика
df_static_annotations_songs_2000_2058 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/song_level/static_annotations_songs_2000_2058.csv')
df_static_annotations_songs_2000_2058.head(5)
| SongId | WorkerId | Arousal_Average | Valence_Average | Arousal_Maximum | Valence_Maximum | Arousal_Minimum | Valence_Minimum | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2001 | 523edc3414996c8e52eb14f20c93fe96 | 7 | 3 | 8 | 7 | 2 | 2 |
| 1 | 2001 | ae0b751438f08bb474e5cd3e48e0d417 | 5 | 3 | 9 | 3 | 3 | 2 |
| 2 | 2001 | fd4cc3ae7434a18286204f133b490502 | 7 | 3 | 8 | 5 | 4 | 2 |
| 3 | 2001 | 58af5138dd2f00af1f52d2c2c031f4a2 | 7 | 2 | 9 | 4 | 5 | 1 |
| 4 | 2001 | 3c960e4e5a02213fc82b98b770a0663c | 7 | 5 | 9 | 6 | 3 | 4 |
#данные 2015 динамика для 2001 песни
df_dynamic_valence_2001 = pd.read_csv('audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)/valence/2001.csv')
df_dynamic_valence_2001
| WorkerId | sample_15000ms | sample_15500ms | sample_16000ms | sample_16500ms | sample_17000ms | sample_17500ms | sample_18000ms | sample_18500ms | sample_19000ms | ... | sample_296000ms | sample_296500ms | sample_297000ms | sample_297500ms | sample_298000ms | sample_298500ms | sample_299000ms | sample_299500ms | sample_300000ms | sample_300500ms | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 883449004b1bf2a07a284f59ddae1fd7 | 0.10 | 0.10 | 0.10 | 0.10 | 0.07 | 0.04 | 0.04 | 0.04 | 0.04 | ... | -0.63 | -0.64 | -0.64 | -0.62 | -0.59 | -0.52 | -0.49 | -0.48 | -0.43 | -0.43 |
| 1 | b09a5957e5d5e47e556d203529a0ae6d | -0.07 | -0.08 | -0.08 | -0.08 | -0.08 | -0.08 | -0.10 | -0.13 | -0.16 | ... | -0.87 | -0.87 | -0.87 | -0.87 | -0.87 | -0.87 | -0.87 | -0.87 | -0.87 | -0.87 |
| 2 | a8d14f76676af36b8978406be47c0c38 | 0.07 | 0.07 | 0.07 | 0.07 | 0.08 | 0.08 | 0.08 | 0.08 | 0.08 | ... | -0.43 | -0.41 | -0.39 | -0.34 | -0.28 | -0.23 | -0.15 | -0.12 | -0.10 | -0.06 |
| 3 | 02bf7a99a5e47f4d52939ab7efc8a549 | -0.60 | -0.60 | -0.60 | -0.57 | -0.54 | -0.54 | -0.49 | -0.49 | -0.46 | ... | -0.75 | -0.94 | -0.95 | -0.98 | -0.96 | -0.96 | -0.96 | -0.82 | -0.66 | -0.63 |
| 4 | 467b1ff3ccfff51e2882bd9d39ef2082 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | -0.03 | ... | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | 0.00 | 0.00 | 0.00 |
5 rows × 573 columns
#количество измерений который сделал каждый работник в 2013 и 2014 статика
dict_workers_freq_s = {}
dict_workers_freq_s = dict_workers_freq_s.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
for i in df_static_annotations_songs_1_2000.index:
dict_workers_freq_s[df_static_annotations_songs_1_2000.loc[i]['workerID']]+=1
dict_workers_freq_s
{'6010bbc8e7ef4b21fa38f9c3a9754ef3': 396,
'3c888e77b992ae3cd2adfe16774e23b9': 11,
'2afd218c3aecb6828d2be327f8b9c46f': 760,
'fd5b08ce362d855ca9152a894348130c': 222,
'9c8073214a052e414811b76012df8847': 160,
'6b492acb1838463dc7c07a76b26313c4': 247,
'8dcd4de7be6479561269b90c1dc8a3b4': 454,
'cbc86e332e889251fe0fa71df472f202': 151,
'259443e9cad56b4f68b664daa20cb323': 305,
'5bf3c29fd496e2cafa9c531c7e9c6d61': 29,
'bf34e3c5724ce07d29ef12db5f767258': 249,
'514278711fcdb6dfb235c03a3aa38ee3': 269,
'4eb43aa7f89260bb40b3970a45a82678': 333,
'ed7ed76453bd846859f5e6b9149df276': 675,
'571d66ea4abfb694d9bb1281d7701710': 68,
'd84d58533b58797bccca5811b4135c95': 31,
'490951556961e4b88fe1a3ac53b3b186': 313,
'2c5952c4f931410e9010eb87c276020d': 73,
'0c37993dcf5f99f7b00d02495ed01bc3': 339,
'64cae7b86c7dcb1b40d17e43c0c2109e': 365,
'24ebcf86498b0f2793b55c5b9a7756b5': 210,
'94d7a0f7c8f2e1c714b0649f835c538c': 150,
'a00de41fdf121f6dfe5db851a575d2d1': 146,
'7cecbffe1da5ae974952db6c13695afe': 428,
'eca1130f44cd2e17645e40a0fa2ef59b': 1005,
'd8e56509dc582df255732a0170323231': 61,
'de0b07c32bb6ca4655487026e0f3fc38': 74,
'3226bc99212ad30ce2ced981de1c437a': 34,
'5097e48e561bf0261b242f211b289825': 53,
'9d1aa48692579ca9330d3c61d10e22e6': 84,
'ce209fb2fe01d6c85dbb29b6fd84b563': 49,
'8eb1abd1acca601d1e23e85c69b1742a': 34,
'7a81cab11e4b2b4aa9c0718c90835611': 30,
'6ca7c27715f54e2f47ebe4e1c9dcf842': 72,
'f0e2dfdbc324f309660b731ad16ce2fd': 17,
'00fa8d61b5f75e16ea71bff7a3def9c9': 30,
'7685fa48defdc384f720bd1edbf17ed6': 55,
'c1edc56f4d30d94a94ee41654c4f6734': 31,
'a2a15d03117e137478c5718212805270': 50,
'9ad2a06dfac81761a123cb40084dcbd7': 23,
'28b56a449c99c88e1f9fff59d0f16aac': 84,
'c795c7de8f350ab35dfd1100a84bf213': 68,
'f8ecfe868f673b16e2f6e5614cb46b7d': 3,
'4b4c4f319a356ea87affb872173a2095': 34,
'20c7d1ee0615a09b0bac9438cc8325e2': 66,
'861d48b913f37227fcf9a7b1e547c0bb': 8,
'be4e771b30940fb2d5f5dcfe3877dd2e': 6,
'4480a650a96730fb629aa7fa2079e7c3': 37,
'a0f5cedc3a2371ec13663226c4b44771': 4,
'cd09cd99c38d4bfb7b554dff21196c22': 26,
'1ee4bd59c9ea084016ff2811dbb00568': 53,
'145b408fa103c16bb00e8734cc39a29f': 18,
'be6ec863f38f611dff4958ccd33cf638': 60,
'7a23690289260d2b8c1d71d8c22b69f0': 18,
'3bf93d5f7b82b46e38feb881d00c5798': 36,
'1bd2dc9e0406a2e240b288685c87fa76': 3,
'212246528da7c13457844fc4453e6019': 22,
'84c2d6fc2e78a1d1283c386c0d4daee4': 9,
'e347f98d0a810ded7c14d7be813a5868': 19,
'04db9e73677c3b8882908ef7ee4a3424': 3,
'ff18a27328ffd40ef52b7ebb7a0ded94': 20,
'3c63524f15931dc8bed7a768cb7d6150': 14,
'807dfc125f09be392a1f1b58c05b3742': 11,
'4dfca2f4dc4e8e6c3b1b7d32e10579c1': 17,
'3deba1aaf448d3857c6c70f63c2a6430': 11,
'855ba3170f3e470170d23a748ccb41ed': 42,
'dd80f1940fe5b84eddde6e83542bd89b': 12,
'2ec24376127909f3be36d770e6ae41a6': 14,
'b8ef6a913a63225faafd661ee2e1a7c0': 10,
'34fb7d43f3a35d4afac7563b7453e31c': 5,
'f3008c23ea8f35ef870e735df67fcc46': 7,
'5fa91ab06a079c59a1d9e8174a488f91': 3,
'504b651e27c89d1d46d101dd87823ab6': 9,
'38531641e6c0628757776b0088bcc854': 7,
'807f0025a626896f04566aa37cfbce0d': 3,
'd0c51e42ea093dc9a9a98ef888637c8e': 2,
'ccfcf36a939a8af15a987fa562a49207': 7,
'7975199043cec980e587889f1639c0a4': 11,
'b13fb0da6dd2f5d7fe398e6e53b52110': 7,
'15ec33e862185406170ff931583b014f': 4,
'c3c21239b85dcdd6679fc212afd02a49': 9,
'478905563bab736f35d94bd7b0a27f9a': 15,
'2f790705ae66e70e81cc0f11ce0f4b9b': 2,
'27f51a4a7fe8565d26cadb88584441e5': 2,
'52b597296a8dca0c5cc5a19231eeff89': 2,
'1065a2110392d3e83d61b4282f55353c': 7,
'6d59196a3f495939a1a776cbebe212ba': 3,
'fa3305c1bc04047d36a8b7a4c77fa81b': 4,
'fecd63ea857606ed61cbfe2ba1a70b3b': 4,
'51f5fb156a5c0051682ee1835e30fbbd': 5,
'd88c800327bffffea5562e23c276ede3': 2,
'594f3fb0d194f109697627b5241f4011': 3,
'a186cdd58a92051b7c73adc9bd6e65ca': 7,
'40b86bbb6927a793c05968b8b95e214d': 13,
'b37092cf23b42f8b8497d8ba89be157a': 2,
'3111e02887b600ee085c72c0a3df33e8': 1,
'd8188e46705e2e87f6a051e0cb338dea': 2,
'19fee46f2810f34a8b69a7768d897a59': 1,
'56a384c7fb2545f9de3e85fa7445d92b': 2,
'e105c200f413d7b2c5850c0df4b9687e': 2,
'a30d244141cb2f51e0803e79bc4bd147': 985,
'8ef6c1b6e31d907a3bba6f4edd303371': 18,
'610d5d89f56667e3d4537c529aae496e': 6,
'78b5e9744073532cc376976b5fc6b2fc': 718,
'de2b2c35312ac2f0a8510743742c0219': 937,
'607f6e34a0b5923333f6b16d3a59cc98': 955,
'bb50b45a1874ede476874bd57e4cabb4': 178,
'ff9c1993d2a21f2117c30d8e295dd4ac': 661,
'b09a5957e5d5e47e556d203529a0ae6d': 708,
'00de940f0b5cfc82cca4791199e3bfb3': 751,
'623681f76a3eab5d9c86fbc0e1ca264b': 12,
'11e5c2ba0236fe3643cac09dbdb96580': 30,
'722ad4c331b7d7bebd697bfbb91ee0e8': 36,
'2db8293d2a35a17f16ddf8e97122ec11': 219,
'b36ffffcd1831441a2a3b60919312ccd': 118,
'6222da90667e5b0de990ce6c26dcfa15': 12,
'883449004b1bf2a07a284f59ddae1fd7': 190,
'accbf566ae920d6260d28454e1ee0d6a': 396,
'4c7a94e0937450ef406135849493ab99': 57,
'2fb527b567d10a3ea0dac783c7c2f364': 6,
'1ac10b86ba5ac18e06eea9f9954ab216': 217,
'46a2289decf79f747406fa91cd92fc27': 333,
'0ca907e233b8bf33bf4eca86fcbfcc8f': 54,
'd5cf45435e35cb9166f51f0a55b74a0e': 75,
'62ef0e23f21039e337b545ccc4851525': 48,
'93f4b8995f647ceb58689b78eb2087ef': 36,
'186575d01f40537515988f1369d395eb': 12,
'd0520656367db74cb316b9a26f60864e': 12,
'55016bfc877fc509861b4aba22ee4f7a': 9,
'f71b423642d3d61dffba53ddee48a7e0': 15,
'd4d0cd1ae06f54909291d34ddd6ab497': 78,
'a9f376da22eb77185afae90b192bf9ac': 48,
'65794ea9f5122952403585a237bc5e52': 3,
'1eb9e2c62709ff203b2d9a558abc0937': 39,
'd633894e43380403a7984858af42ecea': 36,
'26bf37eb1750d163fe01ef21b0033a77': 6,
'51452faff8948ad4780f9b6261e813a7': 15,
'c182e8b2f013e4e3c78cbfb01b8970c8': 30,
'5862049dbaad1750964d91a98930832f': 33,
'6db6afec230a8c59397b5f4d97f5da28': 12,
'5bdea9864567a8614b659bf4b2132ab0': 78,
'0bda2743e12d8a204ab04122658d73a3': 18,
'74f10ac255db9e37db6c6cbf604eb7e0': 18,
'9c3b8737a6a00626867c5e05ee80157e': 3,
'e9fda1f240982b20e0df5ba9c4a337e9': 18,
'888b76dff350bdb6b25783d7c4110081': 12,
'a2a845665afabbc319eb7c5305b0a7ec': 6,
'2461c19378deafd4ec26d3993eeef635': 15,
'c19c053aafbf1492b9e57ce3da5d45c1': 24,
'ca76c553e32c2f096c3bc8f7280ba408': 3,
'027cefa6afc040448d29558b3175cdc1': 9,
'e336e40ae9ac101cec57e8e4323ffb79': 6,
'4c2037269ef8fd0a3349e6b54dd1bb1f': 9,
'248172d9efbbd5eb72d48de6884054ab': 24,
'f7502ffb3616a789e3e5b38f7325b95f': 12,
'3ca50399775f0540bd002cb0d6f54a3d': 6,
'ba85e679d3c3ac42412cb8deaba66a5a': 3,
'49be5653eaba26f1eb60fd9d63f23502': 3,
'9edfd17e8b78d0981868f84c5f20a118': 3,
'6cb1856680ea078c9f46437e2bdcd09e': 6,
'651938620e6e6c78bfa7854784fe62c2': 3,
'fc2fa5656d42b49f3caf01f663085069': 6,
'615d836ba25132081e0ebd2182221a59': 6,
'b6927bdee51077f7868f05f1ed39485f': 18,
'dbb8ffe292aaa9bc2de69ca511af2b3a': 3,
'54cc66fe7cbb01775a6b7c7d703cdeff': 3,
'b4ba75169c585b959f1247fac7e4be7a': 3,
'd3b1de8ed2ffc3eb9af2ef40a80c7d7d': 3,
'0aef6e22005035ac1b93e0a99c961f4c': 6,
'd5b7242bc8a4bb534b8489c24b81fc34': 3,
'374a5659c02e12b01db6319436f17a7d': 3,
'5b044cf509da1d8444b6f60c465240ef': 3,
'77055e16055dc7bc817f7c4bb2eb8fae': 6,
'35536d2d90b93461aa74200967d002e8': 3,
'987a16808cac2496853b6f531c0878cb': 3,
'80db3788bc598d1b32979bea958d9358': 6,
'4dedf223de3f8ebde100df78a5428251': 3,
'2a6b63b7690efa2390c8d9fee11b1407': 3,
'485d8e33a731a830ef0aebd71b016d08': 6,
'b92704ea431d60d62bd8fd18e2940067': 6,
'a4410f548a05bf09b8541ec2672d8490': 3,
'67785f343e6d117fac4d1697697cd8ae': 3,
'da37d1548ffd0631809f7be341e4fe4d': 3,
'ad3b997c4f2382a66e49f035cacfa682': 3,
'a34913ea1010b5812a14d1fef9586a4f': 3,
'659accfd85162122ca361ac34e730c4d': 3,
'9d32be9708a9520ec07e91aec9653291': 3}
#связь частоты и количества выбросов в 2013 и 2014 статика
fig, ax = plt.subplots(figsize=(6,6))
dict_all_outliers_s = {k : dict_arousal_outliers_s.get(k,0)+dict_valence_outliers_s.get(k,0) for k in set(dict_valence_outliers_s)|set(dict_arousal_outliers_s)}
array_freq =[]
array_outliers =[]
for k,v in dict_all_outliers_s.items():
array_freq.append(dict_workers_freq_s[k])
array_outliers.append(dict_all_outliers_s[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
plt.show()
#частота работников в arousal и valence 2014 динамика
dict_worker_freq_ar_d={}
dict_worker_freq_val_d={}
dict_worker_freq_ar_d=dict_worker_freq_ar_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
dict_worker_freq_val_d=dict_worker_freq_val_d.fromkeys(df_static_annotations_songs_1_2000['workerID'].unique(), 0)
for i in df_metadata_2014['Id']:
df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
if ('WorkerId' in df_arousal_d.columns):
for j in df_arousal_d['WorkerId']:
dict_worker_freq_ar_d[j]+=1
df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
if ('WorkerId' in df_valence_d.columns):
for j in df_valence_d['WorkerId']:
dict_worker_freq_val_d[j]+=1
#связь частоты и количества выбросов в 2014 динамика для arousal
fig, ax = plt.subplots(figsize=(6,6))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_outliers_d.items():
array_freq.append(dict_worker_freq_ar_d[k])
array_outliers.append(dict_arousal_outliers_d[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2014 динамика для valence
fig, ax = plt.subplots(figsize=(6,6))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_outliers_d.items():
array_freq.append(dict_worker_freq_val_d[k])
array_outliers.append(dict_valence_outliers_d[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2014 динамика
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_outliers_d.items():
array_freq.append(dict_worker_freq_ar_d[k])
array_outliers.append(dict_arousal_outliers_d[k])
plt.scatter(x=array_freq,y=array_outliers,color='b')
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
array_freq_2 = []
array_outliers_2 =[]
for k,v in dict_valence_outliers_d.items():
array_freq_2.append(dict_worker_freq_val_d[k])
array_outliers_2.append(dict_valence_outliers_d[k])
array_freq = array_freq + array_freq_2
array_outliers = array_outliers+array_outliers_2
plt.scatter(x=array_freq,y=array_outliers,color='b')
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print(array_freq)
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#анализ выбросов для данных 2015
df_metadata_2015 = pd.read_csv('audio_data/metadata/metadata_2015.csv',usecols=['id','Filename','title','artist','album','genre'])
df_metadata_2015.head()
| id | Filename | title | artist | album | genre | |
|---|---|---|---|---|---|---|
| 0 | 2001 | Creepoid_OldTree | Old Tree | Creepoid | NaN | Rock |
| 1 | 2002 | AmarLal_SpringDay1 | Spring Day 1 | Amar Lal | NaN | Singer/Songwriter |
| 2 | 2003 | JoelHelander_ExcessiveResistancetoChange | Excessive Resistance to Change | Joel Helander | NaN | Classical |
| 3 | 2004 | AimeeNorwich_Flying | Flying | Aimee Norwich | NaN | World/Folk |
| 4 | 2005 | AvaLuna_Waterduct | Waterduct | Ava Luna | NaN | Rock |
#все работники оценивающие динамику
directory = 'audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)'
workers_arousal = pd.Series()
workers_valence = pd.Series()
for i in df_metadata_2015['id']:
df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
workers_arousal = pd.concat([workers_arousal,df_arousal_d['WorkerId']], axis=0)
df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
workers_valence = pd.concat([workers_valence,df_valence_d['WorkerId']], axis=0)
workers_arousal_2015 = workers_arousal.unique()
workers_valence_2015 = workers_valence.unique()
dict_worker_freq_ar_d_2015 = {}
dict_worker_freq_ar_d_2015 = dict_worker_freq_ar_d_2015.fromkeys(workers_arousal_2015, 0)
dict_worker_freq_val_d_2015 = {}
dict_worker_freq_val_d_2015 = dict_worker_freq_val_d_2015.fromkeys(workers_valence_2015, 0)
for i in df_metadata_2015['id']:
df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
for j in df_arousal_d['WorkerId']:
dict_worker_freq_ar_d_2015[j]+=1
df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
for j in df_valence_d['WorkerId']:
dict_worker_freq_val_d_2015[j]+=1
C:\Users\0D04~1\AppData\Local\Temp/ipykernel_20668/64907381.py:3: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. workers_arousal = pd.Series() C:\Users\0D04~1\AppData\Local\Temp/ipykernel_20668/64907381.py:4: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. workers_valence = pd.Series()
#подсчитывание выбросов для динамики arousal и valence
dict_arousal_outliers_d_2015 = {}
dict_arousal_outliers_d_2015 = dict_arousal_outliers_d_2015.fromkeys(workers_arousal_2015, 0)
dict_valence_outliers_d_2015 = {}
dict_valence_outliers_d_2015 = dict_valence_outliers_d_2015.fromkeys(workers_valence_2015, 0)
directory = 'audio_data/DEAM_Annotations/annotations/annotations per each rater/dynamic (per second annotations)'
for i in df_metadata_2015['id']:
df_arousal_d = pd.read_csv(glob.glob(directory + '/arousal/' + str(i) + '.csv')[0])
#print(i)
#print(df_arousal_d)
index_series = df_arousal_d['WorkerId']
df_arousal_d_mean = df_arousal_d.drop('WorkerId', axis=1).T.describe().T['mean']
#print(df_arousal_d_mean)
#print(index_series)
df_arousal_d_mean = df_arousal_d_mean.set_axis(index_series)
#print(df_arousal_d_mean)
######
outliers_arousal_d = find_outliers(df_arousal_d_mean)
#print(outliers_arousal_d)
for j in outliers_arousal_d.index:
dict_arousal_outliers_d_2015[j]+=1
df_valence_d = pd.read_csv(glob.glob(directory + '/valence/' + str(i) + '.csv')[0])
#print(i)
#print(df_valence_d)
df_valence_d_mean = df_valence_d.drop('WorkerId', axis=1).T.describe().T['mean']
index_series = df_valence_d['WorkerId']
#print(df_valence_d_mean)
#print(index_series)
df_valence_d_mean = df_valence_d_mean.set_axis(index_series)
#print(df_valence_d_mean)
#####
outliers_valence_d = find_outliers(df_valence_d_mean)
#print(outliers_valence_d)
for j in outliers_valence_d.index:
dict_valence_outliers_d_2015[j]+=1
print(dict_arousal_outliers_d_2015,'\n')
print(dict_valence_outliers_d_2015)
{'883449004b1bf2a07a284f59ddae1fd7': 1, 'b09a5957e5d5e47e556d203529a0ae6d': 18, 'a8d14f76676af36b8978406be47c0c38': 5, '02bf7a99a5e47f4d52939ab7efc8a549': 2, '467b1ff3ccfff51e2882bd9d39ef2082': 0, 'e692af6e00f4a7d14933fd23058998ca': 2, '4874e802dab1e5f13ee4da6403f40d68': 1, '5c71ac012aadbd3491d3cc2a6b52afaa': 1}
{'883449004b1bf2a07a284f59ddae1fd7': 3, 'b09a5957e5d5e47e556d203529a0ae6d': 4, 'a8d14f76676af36b8978406be47c0c38': 2, '02bf7a99a5e47f4d52939ab7efc8a549': 1, '467b1ff3ccfff51e2882bd9d39ef2082': 0, 'e692af6e00f4a7d14933fd23058998ca': 4, '4874e802dab1e5f13ee4da6403f40d68': 4}
#связь частоты и количества выбросов в 2015 динамика для arousal
fig, ax = plt.subplots(figsize=(10,10))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_outliers_d_2015.items():
array_freq.append(dict_worker_freq_ar_d_2015[k])
array_outliers.append(dict_arousal_outliers_d_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2015 динамика для valence
fig, ax = plt.subplots(figsize=(10,10))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_outliers_d_2015.items():
array_freq.append(dict_worker_freq_val_d_2015[k])
array_outliers.append(dict_valence_outliers_d_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#все работники оценивающие статику
workers_static = df_static_annotations_songs_2000_2058[' WorkerId'].unique()
workers_static
array(['523edc3414996c8e52eb14f20c93fe96',
'ae0b751438f08bb474e5cd3e48e0d417',
'fd4cc3ae7434a18286204f133b490502',
'58af5138dd2f00af1f52d2c2c031f4a2',
'3c960e4e5a02213fc82b98b770a0663c',
'949fcd0caf9d4b51a280acfbcd5e8e80',
'b8bc7171b9ff0de8aaed0bf210fb711c',
'b4cad77b1093f7d134c6c141af4dddad'], dtype=object)
#подсчёт выбросов для данных 2015 года
dict_arousal_av_outliers_s_2015 = {}
dict_valence_av_outliers_s_2015 = {}
dict_arousal_max_outliers_s_2015 = {}
dict_valence_max_outliers_s_2015 = {}
dict_arousal_min_outliers_s_2015 = {}
dict_valence_min_outliers_s_2015 = {}
dict_arousal_av_outliers_s_2015 = dict_arousal_av_outliers_s_2015.fromkeys(workers_static, 0)
dict_valence_av_outliers_s_2015 = dict_valence_av_outliers_s_2015.fromkeys(workers_static, 0)
dict_arousal_max_outliers_s_2015 = dict_arousal_max_outliers_s_2015.fromkeys(workers_static, 0)
dict_valence_max_outliers_s_2015 = dict_valence_max_outliers_s_2015.fromkeys(workers_static, 0)
dict_arousal_min_outliers_s_2015 = dict_arousal_min_outliers_s_2015.fromkeys(workers_static, 0)
dict_valence_min_outliers_s_2015 = dict_valence_min_outliers_s_2015.fromkeys(workers_static, 0)
for i in df_metadata_2015['id']:
outliers_arousal_av_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Arousal_Average'])
for j in outliers_arousal_av_outliers_s_2015.index:
dict_arousal_av_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
outliers_valence_av_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Valence_Average'])
for j in outliers_valence_av_outliers_s_2015.index:
dict_valence_av_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
outliers_arousal_max_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Arousal_Maximum'])
for j in outliers_arousal_max_outliers_s_2015.index:
dict_arousal_max_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
outliers_valence_max_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Valence_Maximum'])
for j in outliers_valence_max_outliers_s_2015.index:
dict_valence_max_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
outliers_arousal_min_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Arousal_Minimum'])
for j in outliers_arousal_min_outliers_s_2015.index:
dict_arousal_min_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
outliers_valence_min_outliers_s_2015 = find_outliers(df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i][' Valence_Minimum'])
for j in outliers_valence_min_outliers_s_2015.index:
dict_valence_min_outliers_s_2015[df_static_annotations_songs_2000_2058[df_static_annotations_songs_2000_2058['SongId'] == i].loc[j][' WorkerId']]+=1
print(dict_arousal_av_outliers_s_2015,'\n')
print(dict_valence_av_outliers_s_2015,'\n')
print(dict_arousal_max_outliers_s_2015,'\n')
print(dict_valence_max_outliers_s_2015,'\n')
print(dict_arousal_min_outliers_s_2015,'\n')
print(dict_valence_min_outliers_s_2015)
{'523edc3414996c8e52eb14f20c93fe96': 6, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 5, '58af5138dd2f00af1f52d2c2c031f4a2': 1, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 1, 'b8bc7171b9ff0de8aaed0bf210fb711c': 0, 'b4cad77b1093f7d134c6c141af4dddad': 0}
{'523edc3414996c8e52eb14f20c93fe96': 2, 'ae0b751438f08bb474e5cd3e48e0d417': 4, 'fd4cc3ae7434a18286204f133b490502': 3, '58af5138dd2f00af1f52d2c2c031f4a2': 3, '3c960e4e5a02213fc82b98b770a0663c': 1, '949fcd0caf9d4b51a280acfbcd5e8e80': 5, 'b8bc7171b9ff0de8aaed0bf210fb711c': 4, 'b4cad77b1093f7d134c6c141af4dddad': 0}
{'523edc3414996c8e52eb14f20c93fe96': 2, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 0, '58af5138dd2f00af1f52d2c2c031f4a2': 1, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 1, 'b8bc7171b9ff0de8aaed0bf210fb711c': 0, 'b4cad77b1093f7d134c6c141af4dddad': 0}
{'523edc3414996c8e52eb14f20c93fe96': 0, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 5, '58af5138dd2f00af1f52d2c2c031f4a2': 5, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 6, 'b8bc7171b9ff0de8aaed0bf210fb711c': 5, 'b4cad77b1093f7d134c6c141af4dddad': 0}
{'523edc3414996c8e52eb14f20c93fe96': 5, 'ae0b751438f08bb474e5cd3e48e0d417': 1, 'fd4cc3ae7434a18286204f133b490502': 6, '58af5138dd2f00af1f52d2c2c031f4a2': 1, '3c960e4e5a02213fc82b98b770a0663c': 0, '949fcd0caf9d4b51a280acfbcd5e8e80': 4, 'b8bc7171b9ff0de8aaed0bf210fb711c': 0, 'b4cad77b1093f7d134c6c141af4dddad': 0}
{'523edc3414996c8e52eb14f20c93fe96': 3, 'ae0b751438f08bb474e5cd3e48e0d417': 3, 'fd4cc3ae7434a18286204f133b490502': 1, '58af5138dd2f00af1f52d2c2c031f4a2': 7, '3c960e4e5a02213fc82b98b770a0663c': 1, '949fcd0caf9d4b51a280acfbcd5e8e80': 2, 'b8bc7171b9ff0de8aaed0bf210fb711c': 5, 'b4cad77b1093f7d134c6c141af4dddad': 0}
#количество измерений работников
dict_worker_freq_s_2015 = {}
dict_worker_freq_s_2015 = dict_worker_freq_s_2015.fromkeys(workers_static, 0)
for i in df_static_annotations_songs_2000_2058.index:
dict_worker_freq_s_2015[df_static_annotations_songs_2000_2058.loc[i][' WorkerId']]+=1
print(dict_worker_freq_s_2015)
{'523edc3414996c8e52eb14f20c93fe96': 58, 'ae0b751438f08bb474e5cd3e48e0d417': 59, 'fd4cc3ae7434a18286204f133b490502': 58, '58af5138dd2f00af1f52d2c2c031f4a2': 36, '3c960e4e5a02213fc82b98b770a0663c': 2, '949fcd0caf9d4b51a280acfbcd5e8e80': 50, 'b8bc7171b9ff0de8aaed0bf210fb711c': 34, 'b4cad77b1093f7d134c6c141af4dddad': 1}
#связь частоты и количества выбросов в 2015 статика для arousal_average
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_av_outliers_s_2015.items():
array_freq.append(dict_worker_freq_s_2015[k])
array_outliers.append(dict_arousal_av_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2015 статика для valence_average
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_av_outliers_s_2015.items():
array_freq.append(dict_worker_freq_s_2015[k])
array_outliers.append(dict_valence_av_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2015 статика для arousal_maximum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_max_outliers_s_2015.items():
array_freq.append(dict_worker_freq_s_2015[k])
array_outliers.append(dict_arousal_max_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2015 статика для valence_maximum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_max_outliers_s_2015.items():
array_freq.append(dict_worker_freq_s_2015[k])
array_outliers.append(dict_valence_max_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2015 статика для arousal_minimum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_arousal_min_outliers_s_2015.items():
array_freq.append(dict_worker_freq_s_2015[k])
array_outliers.append(dict_arousal_min_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#связь частоты и количества выбросов в 2015 статика для valence_minimum
fig, ax = plt.subplots(figsize=(5,5))
array_freq =[]
array_outliers =[]
for k,v in dict_valence_min_outliers_s_2015.items():
array_freq.append(dict_worker_freq_s_2015[k])
array_outliers.append(dict_valence_min_outliers_s_2015[k])
plt.scatter(x=array_freq,y=array_outliers)
plt.xlabel('Количество всех измерений работников')
plt.ylabel('Количество всего выбросов')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(array_freq, array_outliers, 1)
plt.plot(np.array(array_freq),m*np.array(array_freq)+b,color='r')
plt.show()
#сравнение количества выбросов в arousal в статике и динамике в 2015
plt.bar(['static_arousal_2015','dynamic_arousal_2015'], [sum(dict_arousal_av_outliers_s_2015.values()),sum(dict_arousal_outliers_d_2015.values())])
plt.show()
#сравнение количества выбросов в valence в статике и динамике в 2015
plt.bar(['static_valence_2015','dynamic_valence_2015'], [sum(dict_valence_av_outliers_s_2015.values()),sum(dict_valence_outliers_d_2015.values())])
plt.show()
#график всех песен в 2013-2014 годах построенный по arousal и valence статика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=normilize(df_static_averaged_1_2000[' arousal_mean']),x=normilize(df_static_averaged_1_2000[' valence_mean']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
dict_quarters_s ={1:0,2:0,3:0,4:0}
for i in df_static_averaged_1_2000.index:
arousal = normilize(df_static_averaged_1_2000.loc[i][' arousal_mean'])
valence = normilize(df_static_averaged_1_2000.loc[i][' valence_mean'])
#print(arousal, valence)
if ((arousal>0) and (valence>0)):
dict_quarters_s[1]+=1
if ((arousal>0) and (valence<=0)):
dict_quarters_s[2]+=1
if ((arousal<=0) and (valence<=0)):
dict_quarters_s[3]+=1
if ((arousal<=0) and (valence>0)):
dict_quarters_s[4]+=1
print(dict_quarters_s)
{1: 583, 2: 217, 3: 723, 4: 221}
#график всех песен в 2013-2014 годах построенный по arousal и valence динамика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=df_dynamic_arousal.loc[1:2000].T.describe().T['mean'], x=df_dynamic_valence.loc[1:2000].T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
dict_quarters_d={1:0,2:0,3:0,4:0}
for i in set(df_dynamic_arousal.loc[1:2000].index):
arousal = df_dynamic_arousal.loc[i].T.describe().T['mean']
valence = df_dynamic_valence.loc[i].T.describe().T['mean']
#print(arousal, valence)
if ((arousal>0) and (valence>0)):
dict_quarters_d[1]+=1
if ((arousal>0) and (valence<=0)):
dict_quarters_d[2]+=1
if ((arousal<=0) and (valence<=0)):
dict_quarters_d[3]+=1
if ((arousal<=0) and (valence>0)):
dict_quarters_d[4]+=1
print(dict_quarters_d)
{1: 954, 2: 220, 3: 374, 4: 196}
#график всех песен в 2015 построенный по arousal и valence статика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=normilize(df_static_averaged_2000_2058[' arousal_mean']), x=normilize(df_static_averaged_2000_2058[' valence_mean']))
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
dict_quarters_s_2015 ={1:0,2:0,3:0,4:0}
for i in df_static_averaged_2000_2058.index:
arousal = normilize(df_static_averaged_2000_2058.loc[i][' arousal_mean'])
valence = normilize(df_static_averaged_2000_2058.loc[i][' valence_mean'])
#print(arousal, valence)
if ((arousal>0) and (valence>0)):
dict_quarters_s_2015[1]+=1
if ((arousal>0) and (valence<=0)):
dict_quarters_s_2015[2]+=1
if ((arousal<=0) and (valence<=0)):
dict_quarters_s_2015[3]+=1
if ((arousal<=0) and (valence>0)):
dict_quarters_s_2015[4]+=1
print(dict_quarters_s_2015)
{1: 11, 2: 12, 3: 19, 4: 16}
#график всех песен в 2015 построенный по arousal и valence динамика
fig = plt.figure(figsize=(5,5))
ax = fig.add_subplot(1, 1, 1)
ax.spines['left'].set_position('center')
ax.spines['bottom'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
plt.scatter(y=df_dynamic_arousal.loc[2000:2058].T.describe().T['mean'], x=df_dynamic_valence.loc[2000:2058].T.describe().T['mean'])
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.show()
dict_quarters_d_2015={1:0,2:0,3:0,4:0}
for i in set(df_dynamic_arousal.loc[2000:2058].index):
arousal = df_dynamic_arousal.loc[i].T.describe().T['mean']
valence = df_dynamic_valence.loc[i].T.describe().T['mean']
#print(arousal, valence)
if ((arousal>0) and (valence>0)):
dict_quarters_d_2015[1]+=1
if ((arousal>0) and (valence<=0)):
dict_quarters_d_2015[2]+=1
if ((arousal<=0) and (valence<=0)):
dict_quarters_d_2015[3]+=1
if ((arousal<=0) and (valence>0)):
dict_quarters_d_2015[4]+=1
print(dict_quarters_d_2015)
{1: 23, 2: 16, 3: 11, 4: 9}
#сравнение количества песен, попавших в одну четверть, 2013-2014
X=dict_quarters_s.keys()
X_axis = np.arange(len(X))
plt.bar(X_axis - 0.2, dict_quarters_s.values(), 0.4, label = 'static')
plt.bar(X_axis + 0.2, dict_quarters_d.values(), 0.4, label = 'dynamic')
plt.xticks(X_axis, X)
plt.legend()
plt.show()
#сравнение количества песен, попавших в одну четверть, 2015
X=dict_quarters_s.keys()
X_axis = np.arange(len(X))
plt.bar(X_axis - 0.2, dict_quarters_s_2015.values(), 0.4, label = 'static')
plt.bar(X_axis + 0.2, dict_quarters_d_2015.values(), 0.4, label = 'dynamic')
plt.xticks(X_axis, X)
plt.legend()
plt.show()
#связь valence_dynamic и valence_static
fig, ax = plt.subplots(figsize=(5,5))
X = df_dynamic_valence.T.describe().T['mean']
Y = pd.concat([df_static_averaged_1_2000[' valence_mean'],df_static_averaged_2000_2058[' valence_mean']])
plt.scatter(X,Y)
plt.xlabel('valence_dynamic')
plt.ylabel('valence_static')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(X, Y, 1)
plt.plot(np.array(X),m*np.array(X)+b,color='r')
plt.show()
#связь arousal_dynamic и arousal_static
fig, ax = plt.subplots(figsize=(5,5))
X = df_dynamic_arousal.T.describe().T['mean']
Y = pd.concat([df_static_averaged_1_2000[' arousal_mean'],df_static_averaged_2000_2058[' arousal_mean']])
plt.scatter(X,Y)
plt.xlabel('arousal_dynamic')
plt.ylabel('arousal_static')
#print([i[0] for i in dict_all_outliers.items()],'\n')
#print([i[0] for i in dict_workers_freq_s.items()])
m, b = np.polyfit(X, Y, 1)
plt.plot(np.array(X),m*np.array(X)+b,color='r')
plt.show()
#сравнение количества песен, попавших в одну четверть
X=dict_quarters_s.keys()
X_axis = np.arange(len(X))
dict_quarters_s_all = {k : dict_quarters_s.get(k,0)+dict_quarters_s_2015.get(k,0) for k in set(dict_quarters_s)}
dict_quarters_d_all = {k : dict_quarters_d.get(k,0)+dict_quarters_d_2015.get(k,0) for k in set(dict_quarters_d)}
plt.bar(X_axis - 0.2, dict_quarters_s_all.values(), 0.4, label = 'static')
plt.bar(X_axis + 0.2, dict_quarters_d_all.values(), 0.4, label = 'dynamic')
plt.xticks(X_axis, X)
plt.legend()
plt.show()
#статистическое распределение попаданий в четверти
plt.pie(dict_quarters_s_all.values(),labels=dict_quarters_s_all.keys(),autopct= '%1.1f%%')
plt.show()
#динамическое распределение попаданий в четверти
plt.pie(dict_quarters_d_all.values(),labels=dict_quarters_d_all.keys(),autopct= '%1.1f%%')
plt.show()
На основе arousal и valence в df_static_averaged_1_2000 и df_static_averaged_2000_2058 создадим новый столбец, содержащий номер класса для данного фрагмента, следующим образом: 
#создаем столбец с индексом класса
def index_class(arousal, valence):
if ((arousal>0) and (valence>0)):
return 1
if ((arousal>0) and (valence<=0)):
return 2
if ((arousal<=0) and (valence<=0)):
return 3
if ((arousal<=0) and (valence>0)):
return 4
indexes_class_1_2000 = [index_class(normilize(df_static_averaged_1_2000[' arousal_mean'].loc[i]), normilize(df_static_averaged_1_2000[' valence_mean'].loc[i])) for i in df_static_averaged_1_2000.index]
df_static_averaged_1_2000["class"] = indexes_class_1_2000
indexes_class_2000_2058 = [index_class(normilize(df_static_averaged_2000_2058[' arousal_mean'].loc[i]), normilize(df_static_averaged_2000_2058[' valence_mean'].loc[i])) for i in df_static_averaged_2000_2058.index]
df_static_averaged_2000_2058["class"] = indexes_class_2000_2058
df_static_averaged_1_2000.head()
| valence_mean | valence_std | arousal_mean | arousal_std | class | |
|---|---|---|---|---|---|
| song_id | |||||
| 2 | 3.1 | 0.94 | 3.0 | 0.63 | 3 |
| 3 | 3.5 | 1.75 | 3.3 | 1.62 | 3 |
| 4 | 5.7 | 1.42 | 5.5 | 1.63 | 1 |
| 5 | 4.4 | 2.01 | 5.3 | 1.85 | 2 |
| 7 | 5.8 | 1.47 | 6.4 | 1.69 | 1 |
df_static_averaged_2000_2058.head()
| valence_mean | valence_std | valence_ max_mean | valence_max_std | valence_min_mean | valence_min_std | arousal_mean | arousal_std | arousal_max_mean | arousal_max_std | arousal_min_mean | arousal_min_std | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| song_id | |||||||||||||
| 2001 | 3.2 | 0.98 | 5.0 | 1.41 | 2.2 | 0.98 | 6.6 | 0.80 | 8.6 | 0.49 | 3.4 | 1.02 | 2 |
| 2002 | 6.4 | 0.49 | 8.2 | 0.98 | 5.0 | 1.10 | 5.2 | 1.17 | 7.4 | 1.36 | 2.2 | 1.17 | 1 |
| 2003 | 5.4 | 1.50 | 7.2 | 1.17 | 3.4 | 1.02 | 4.6 | 1.85 | 6.2 | 2.04 | 1.4 | 0.49 | 4 |
| 2004 | 5.0 | 1.10 | 6.4 | 1.02 | 3.2 | 1.17 | 4.8 | 1.60 | 6.0 | 2.28 | 2.8 | 0.98 | 3 |
| 2005 | 3.8 | 1.17 | 5.0 | 1.10 | 1.6 | 0.80 | 5.2 | 0.75 | 8.4 | 0.80 | 2.0 | 1.10 | 2 |
#!pip install librosa
#!pip3 install pydub
#!pip install ffmpeg-python
import IPython.display as ipd
import librosa
import librosa.display
from pydub import AudioSegment
### 1 class song 7.mp3
sound = AudioSegment.from_mp3('audio_data/DEAM_audio/MEMD_audio/7.mp3')
sound.export('audio_data/DEAM_audio/7.wav', format='wav')
filename ='audio_data/DEAM_audio/7.wav'
plt.figure(figsize=(14,5))
song7,sample_rate=librosa.load(filename)
librosa.display.waveshow(song7,sr=sample_rate)
ipd.Audio(filename)